Multi-aspectual interview technique (M.A.I.T.)

Kane, SC

Abstract

This is exploratory research which employs qualitative interview techniques that are
built on the foundation of Dooyeweerd's Aspects (1984), which is facilitated here by
the Multi-Aspectual Knowledge Elicitation (M.A.K.E) model (Winfield 2(00) and
led to the design and application of the Multi-Aspectual Interview Technique
(M.A.I.T).
This study began when the researcher saw an anomaly in mature students who study
information technology as they have decided to study a subject for which their time
in mandatory state education has provided no foundation of knowledge. Even though
this is true, it should be noted that information technology (I.T.) is categorised by the
Education and Skills Council as one of the preferred subjects for mature students
(Ross 2(02). The information collected for this project was arrived at by carrying
out interviews of mature students studying for an information technology
(I.T) information systems (I.S.) degree at a North-West university, and mature
students studying a health care/nursing, access to university course of which I. T. is a
mandatory part (in the form of the ECDL qualification - European Computer
Driving licence). The second group were studying at a North-West Further
Education college.
This research is an exploration of the experiences of these two groups of mature
students in education. However, the unexpected challenge of how to uncover their
experiences soon made itself an equal focus of the research. Having found and
applied a format (Dooyeweerd's Suite of Aspects) to guide the collection of
everyday experience, the research produced a range of information about the
experiences of people who have both specifically decided to study I.T. and those
who have found themselves studying I.T. as a mandatory part of another course.
Equally represented here is an assessment of how Dooyeweerd's Suite of Aspects
(1984) facilitates qualitative research interviews.

Citation

Kane, S. Multi-aspectual interview technique (M.A.I.T.). (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Jul 13, 2017
• Publicly Available Date: Jul 13, 2017
• Publisher URL: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491780
• Related Public URLs: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491780
• Additional Information: Funders : Engineering and Physical Sciences Research Council (EPSRC)
• Award Date: Dec 1, 2005